PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Inverse problems formulated in terms of first-kind Fredholm integral equations in indirect measurements

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Direct measurements of many properties of real-world systems are not possible. Information on these properties can only be inferred from the result of measurements of other quantities which may be measured directly. The process comprising direct measurements of certain characteristics of the object followed by inference on its sought-for properties from the directly measured characteristics based on a mathematical relation between unknown properties and measured characteristics is called indirect measurement, whereas inference is referred to as an inverse problem in indirect measurement. In general an inverse problem consists either in determining the characteristics of a system under study, driven by controlled or known exciting signals, or in reconstructing exciting signals acting on a system whose internal characteristics are known. In both cases, it is formulated in terms of a mathematical model relating unknown and measured characteristics and signals. One can distinguish continuous and discrete inverse problems, depending on whether the measured and sought-for quantities are represented by functions or by vectors (tuples), respectively. Very many nontrivial inverse problems in indirect measurements are ill-posed which means that they have no solution or the solution exists but is non-unique or unstable, i.e. very small disturbances in the measurement data result in large disturbances in the result of inference. High error amplification is referred to as ill-conditioning. Ill-posedness and ill-conditioning result from the lack of information on sought-for quantities, carried by the measurement data. Therefore, a priori knowledge about the space of admissible solutions has to be employed for solving such inverse problems. The theory of inverse problems and - in particular - effective numerical methods for solving them are of great importance for measurement science and technology; they are crucial for the development of many measurement, imaging and diagnostic techniques. Indirect measurements may be formulated using various mathematical models of the measurement object followed by a measuring system. A broad class of inverse problems, being of importance for indirect measurements, is formulated in terms of Fredholm integral equations of the first kind. These problems are ill-posed and strongly ill-conditioned after discretization. Therefore, sophisticated inverse procedures, utilizing various kinds of a priori knowledge, are applied for solving them. In this paper, theoretical and numerical aspects of inverse problem in indirect measurements are reviewed. In particular the concept of generalized solution (pseudosolution) and the notion of well-posedness is presented and analysed. The review is focused on inverse problems formulated in terms of Fredholm integral equations of the first kind: a general presentation of such problems, at the level of functional analysis, is followed by an overview of numerical aspects of their discretized versions. A concise presentation of selected groups of numerical methods, called inverse methods, for solving inverse problems is also provided.
Rocznik
Strony
333--357
Opis fizyczny
Bibliogr. 85 poz., rys., wzory
Twórcy
autor
  • Wroclaw University of Technology, Faculty of Electronics, Chair of Electronic and Photonic Metrology, B. Prusa 53/55, 50-317 Wrocław, Poland, janusz.mroczka@pwr.wroc.pl
Bibliografia
  • [1] Handbook of Measurement Science, vol. 1: Theoretical Fundamentals, P.H. Sydenham, Wiley-Interscience, New York, 1982.
  • [2] International vocabulary of metrology - Basic and general cocncepts and associated terms (VIM), JCGM, 2008.
  • [3] S.H. Khan, L. Finkelstein: „Advances and Generic Problems in Instrument Design Methodology”. Metrol Meas Syst, vol. 14, no. 1, 2007, pp. 39-58.
  • [4] M. Darowski, W. Klonowski, M. Kozarski, G. Ferrari, K. Zieliński, R. Stepien: “Hybrid Modeling of Biomedical Systems and Measuring Nonlinear Characteristics of Biosignals for Improving Quality of Life”. Metrol. Meast Syst, vol. 14, no. 1, 2007, pp. 89-100.
  • [5] V.V. Vasin, A.L. Ageev: Ill-Posed Problems with A Priori Information. VSP, Utrecht, 1995.
  • [6] M. Kandlikar, G. Ramachandran: “Inverse Methods for Analysing Aerosol Spectrometer Measurements: A Critical Review”. J. Aerosol Sci., vol. 30, no. 4, 1999, pp. 413-437.
  • [7] J. Lal-Jadziak, S. Sienkowski: “Models of Bias of Mean Square Value Digital Estimator for Selected Deterministic and Random Signals”. Metrol Meas Syst, vol. 15, no. 1, 2008, pp. 55-68.
  • [8] P.M. Ramos, T. Radil, A.C. Serra: “Four-Parameter Sine-Fitting Algorithm for Detection and Classification of Transients and Waveform Distorsions”. Metrol Meas Syst, vol. 15, no. 4, 2008, pp. 441-456.
  • [9] B. Barkey, S.E. Paulson, A. Chung: “Genetic Algorithm Inversion of Dual Polarization Polar Nephelometer Data to Determine Aerosol Refractive Index”. J. Aerosol Sci. and Technol., vol. 41, 2007, pp. 751-760.
  • [10]R.J.W. Hodgson: „Genetic Algorithm Approach to Particle Identification by Light Scattering”. Journal of Colloid and Interface Science, vol. 229, 2000, pp. 399-406.
  • [11]J. Vargas-Ubera, J.J. Sánchez-Escobar, J.F. Aguilar, D.M. Gale: “Numerical study of particle-size distributions retrieved from angular light-scattering data using an evolution strategy with the Fraunhofer approximation”. Appl. Opt., vol. 46, no. 17, 2007, pp. 3602-3610.
  • [12]M. Ye, S. Wang, Y. Lu, T. Hu, Z. Zhu, Y. Xu: “Inversion of particle-size distribution from angular lightscattering data with genetic algorithms”. Appl. Opt., vol. 38, no. 12, 1999, pp. 2677-2685.
  • [13]P. Burnos, J. Gajda, P. Piwowar, R. Sroka, M. Stencel, T. Żegleń: “Measurements of Road Traffic Parameters Using Inductive Loops And Piezoelectric Sensors”. Metrol Meas Syst, vol. 14, no. 2, 2007, pp.187-204.
  • [14]P. Burnos, J. Gajda, P. Piwowar, R. Sroka, M. Stencel, T. Żegleń: “Accurate Weighing of Moving Vehicles”. Metrol Meas Syst, vol. 14, no. 4, 2007, pp. 507-516.
  • [15]J. Gajda, R. Sroka, T. Żegleń: “Accuracy Analysis of WIM Systems Calibrated Using Pre-Weighed Vehicles Method”. Metrol Meas Syst, vol. 14, no. 4, 2007, pp. 517-528.
  • [16]A. Woźniak, Maciej Kretkowski, R. Jabłoński: “Measurement Method of Shape Deviation of Cylindrical Micro-Lenses Using Focused Laser Beam and Reference Axis”. Metrol Meas Syst, vol. 14, no. 3, 2007, pp. 351-360.
  • [17]R. Kłosiński: “Periodically Variable Two-Terminal Impedance Description and Measuring Methods”. Metrol Meas Syst, vol. 14, no. 3, 2007, pp. 375-390.
  • [18]J. Piwowarczyk, K. Pacholski: “Utilization of Levenberg-Marquardt’s Method for Identification of the Electronic Current Transducer with a Hall Effect Sensor in a Feedback Loop”. Metrol Meas Syst, vol. 15, no. 1, 2008, pp. 91-104.
  • [19]S.H.R. Ali: “The Influence of Fitting Algorithm and Scanning Speed on Roundness Error for 50 mm Standard Ring Measurement Using CMM”. Metrol Meas Syst, vol. 15, no. 1, 2008, pp. 33-54.
  • [20]W. Menke: Geophysical Data Analysis: Discrete Inverse Theory. Academic Press, San Diego, 1989.
  • [21]G.T. Herman, H.K. Tuy, K.J. Langenberg, P.C. Sabatier: Basic Methods of Tomography and Inverse Problems. Adam Hilger, Bristol and Philadelphia, 1987.
  • [22]A.L. Fymat, V.E. Zuev: Remote Sensing of the Atmosphere: Inversion Methods and Applications. Elsevier, Amsterdam-Oxford-New York, 1978.
  • [23]R.Z. Morawski: „Spectrophotometric applications of digital signal processing”. Meas. Sci. Technol., vol. 17, 2006, pp. 117-144.
  • [24]R.Z. Morawski, C. Niedziński: “Application of a Bayesian Estimator for Identification of Edible Oils on the Basis of Spectrophotometric Data”. Metrol Meas Syst, vol. 15, no. 3, 2008, pp. 247-266.
  • [25]A. Latała, R.Z. Morawski: “Comparison of LS-Type Methods for Determination of Olive Oil Mixtures on the Basis of NIR Spectral Data”. Metrol Meas Syst, vol. 15, no. 4, 2008, pp. 409-420.
  • [26]D.A. Ligon, J.B. Gillespie, P. Pellegrino: “Aerosol properties from spectral extinction and backscatter estimated by an inverse Monte Carlo method”. Appl. Opt., vol. 39, no. 24, 2000, pp. 4402-4410.
  • [27]D.A. Ligon, T.W. Chen, J.B. Gillespie: “Determination of aerosol parameters from light-scattering data using an inverse Monte Carlo technique”. Appl. Opt., vol. 35, no. 21, 1996, pp. 4297-4303.
  • [28]J. Vargas-Ubera, J.F. Aguilar, D.M. Gale: “Reconstruction of particle-size distributions from light-scattering patterns using three inversion methods”. Appl. Opt., vol. 46, no. 1, 2007, pp. 124-132.
  • [29]N. Riefler, T. Wriedt: “Intercomparison of Inversion Algorithms for Particle-Sizing using Mie Scattering”. Part. Part. Syst. Charact., vol. 25, no. 3, 2008, pp. 216-230.
  • [30]A. Voutilainen, J.P. Kaipio: “Estimation of non-stationary aerosol size distributions using the state-space approach”. J. Aerosol Sci. and Technol., vol. 32, 2001, pp. 631-648.
  • [31]A. Voutilainen, F. Stratmann, J.P. Kaipio: “A non-homogeneous regularization method for the estimation of narrow aerosol size distributions”. J. Aerosol Sci. and Technol., vol. 31, no. 12, 2000, pp. 1433-1445.
  • [32]X. Sun, H. Tang, J. Dai: “Inversion of particle size distribution from spectral extinction data using the modified beta function”. Powder Technology, vol. 190, 2009, pp. 292-296.
  • [33]J. Mroczka: “Method of moments in light scattering data inversion in the particle size distribution”. Opt. Comm., vol. 99, 1993, pp. 147-151.
  • [34]J. Mroczka: “Integral transform technique in particle sizing”. J. Aerosol Sci., vol. 20, 1989, pp. 1075-1077.
  • [35]J. Mroczka: “Light scattering at a small angle with the use of Lorenz-Mie theory in particle sizing”. Part. Part. Syst. Charact., vol. 6, 1989, pp. 86-88.
  • [36]M. Czerwiński, J. Mroczka, T. Girasole, G. Gouesbet, G. Grehan: “Light-transmittance predictions under multiple-light-scattering conditions”. Part 1. Direct problem: hybrid-method approximation. Applied Optics., vol. 40, no. 9, 2001, pp. 1514-1524.
  • [37]M. Czerwiński, J. Mroczka, T. Girasole, G. Gouesbet, G. Grehan: “Light Transmitance Predictions under Multiple Light Scattering Conditions”. Part 2: Inverse Problem - Particle Size Determination. Appl. Opt., vol. 40, no. 9, 2001, pp. 1525-1531.
  • [38]T. Girasole, J.N. le. Toulouzan, J. Mroczka, D. Wysoczański: “Fiber orientation and concentration analysis by light scattering: experimental setup and diagnosis”. Review of Scientific Instruments, vol. 68, no. 7, 1997, pp. 2805-2811.
  • [39]T. Girasole, G. Gouesbet, G. Grehan, J.N. le Toulouzan, J. Mroczka, K.F. Ren, D. Wysoczański: “Cylindrical fibre orienation analysis by light scattering: Pt. 2. Experimental aspects”. Part. Syst. Charact., vol. 14, 1997, pp. 211-218.
  • [40]J. Mroczka, R. Szczepanowski: “Modeling of light transmittance measurement in a finite layer of whole blood - a collimated transmittance problem in Monte Carlo simulation and diffusion model”. Optica Applicata, vol. 35, no. 2, 2005, pp. 311-331.
  • [41]A.N. Tikhonov, V.Y. Arsenin: Solutions of Ill-Posed Problems. Winston & Sons, Washington D.C, 1977.
  • [42]O.C. Lingjearde., N. Christophersen: Regularization Principles, Solving Ill-Posed Inverse Problems. 1998
  • [43]A.N. Tikhonov, A.V. Goncharsky: Ill-Posed Problems in Natural Sciences. MIR Publishers, Moscow, 1987.
  • [44]V.A. Morozov: Methods for Solving Incorrectly Posed Problems. Springer Verlag, New York, 1984.
  • [45]J.G. Crump, J.H. Seinfeld: “A new algorithm for inversion of aerosol size distribution data”. Aerosol Sci and Technol., vol. 1, 1982, pp. 15-34.
  • [46]P.C. Hansen: “Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems”. Numerical Algorithms, vol. 6, 1994, pp. 1-35.
  • [47]G. Viera, M.A. Box: “Information content analysis of aerosol remote-sensing experiments using an analytic eigenfunction theory: anomalous diffraction approximation”. Appl. Opt., vol. 24, no. 24, 1985, pp. 4525-4533.
  • [48]S. Brandt: Data Analysis. Springer-Verlag, New York, 1998.
  • [49]A. Björck, G. Dahlquist: Numerical Methods. Prentice-Hall, Englewood Cliffs, New Jersey, 1974.
  • [50]P.C. Hansen: „Computation of the singular value expansion”. Computing, vol. 40, 1988, pp. 185-199.
  • [51]P.C. Hansen: Rank-Deficient and Discrete Ill-Posed Problems. Numerical Aspects of Linear Inversion. SIAM, Philadelphia, 1997.
  • [52]P.C. Hansen: “Regularization, GSVD and truncated GSVD”. BIT, vol. 29, 1989, pp. 491-504.
  • [53]P.C. Hansen: “Relations between SVD and GSVD of discrete regularization problems in standard and general form”. Lin. Alg. Appl., vol. 141, 1990, pp. 165-176.
  • [54]P.C. Hansen: “The truncated SVD as a method for regularization”. BIT, vol. 27, 1987, pp. 543-553.
  • [55]P.C. Hansen, T. Sekii, H. Shibahashi: “The modified truncated SVD method for regularization in general form”. SIAM J. Sci. Stat. Comput., vol. 13, 1992, pp. 1142-1150.
  • [56]P.C. Hansen: “Truncated SVD solutions to discrete ill-posed problems with ill-determined numerical rank”. SIAM J. Sci. Stat. Comput., vol. 11, 1990, pp. 503-518.
  • [57]A.N. Tikhonov: “Solution of incorrectly formulated problems and the regularization method”. Dokl. Akad. Nauk. SSSR, vol. 151, 1963, pp. 501-504 = Soviet Math. Dokl., vol. 4, 1963, pp. 1035-1038.
  • [58]S. Twomey: “On the Numerical Solution of Fredholm Integral Equations of the First Kind by the Inversion of the Linear System Produced by Quadrature”. J. Assoc. Comp. Mach., vol. 10, no. 1, 1963, pp. 97-101.
  • [59]D.L. Phillips: “A Technique for the Numerical Solution of Certain Integral Equations of the First Kind”. J. Assoc. Comp. Mach., vol. 9, no. 1, 1962, pp. 84-97.
  • [60]F. Stout, J.H. Kalivas: “Tikhonov regularization in standardized and general form for multivariate calibration with application towards removing unwanted spectral artifacts”. J. Chemometrics, vol. 20, 2006, pp. 22-33.
  • [61]W.F. Massy: „Principal components regression in exploratory statistical research”. J. Amer. Statist. Assoc., vol. 60, 1965, pp. 234-246.
  • [62]A.E. Hoerl: “Application of ridge analysis to regression problems”. Chem. Eng. Progress, vol. 58, 1962, pp. 54-59.
  • [63]A.E. Hoerl, R.W. Kennard: “Ridge regression: biased estimation for non-orthogonal problems”. Technometrics, vol. 12, 1970, pp. 55-62.
  • [64]C. Ray Smith, W.T. Grandy, Jr. (Eds.): Maximum-Entropy and Bayesian Methods in Inverse Problems. Reidel, Boston, 1985.
  • [65]R.D. Fierro, G.H. Golub, P.C. Hansen, D.P. O’Leary: “Regularization by truncated total least squares”. SIAM J. Sci. Comput., vol. 18, 1997, pp. 1223-1241.
  • [66]R.D. Fierro, J.R. Bunch: “Collinearity and total least squares”. SIAM J. Matrix Anal. Appl., vol. 15, 1994, pp. 1167-1181.
  • [67]S. Wold, A. Ruhe, H. Wold, W.J. Dunn III: “The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses”. SIAM J. Sci. Stat. Comput., vol. 5, 1984, pp. 735-743.
  • [68]H. Brakhage: “On ill-posed problems and the method of conjugate gradients”. H.W. Engl, C.W. Groetsch (Eds.): Inverse and Ill-Posed Problems, Academic Press, Boston, 1987.
  • [69]C.C. Paige, M. A. Saunders: “LSQR: an algorithm for sparse linear equations and sparse least squares”. ACM Trans. Math. Software, vol. 8, 1982, pp. 43-71.
  • [70]D.W. Marquardt: “Generalized inverses, ridge regression, biased linear estimation and nonlinear estimation”. Technometrics, vol. 12, 1970, pp. 591-612.
  • [71]G.H. Golub, M. Heath, H. Wahba: “Generalized cross validation as a method for choosing a good ridge parameter”. Technometrics, vol. 21, 1979, pp. 215-224.
  • [72]P.C. Hansen, D.P. O’Leary: The use of the L-curve in the regularization of discrete ill-posed problems. SIAM J. Sci. Comput., vol. 14, 1993, pp. 1487-1503.
  • [73]C.R. Vogel: “Optimal choice of a truncation level for the truncated SVD solution of linear first kind integral equation when data are noisy”. SIAM J. Numer. Anal., vol. 23, 1986, pp. 109-117.
  • [74]M.T. Chahine: “Determination of the temperature profile in an atmosphere from its outgoing radiance”. J. Opt. Soc. Am., vol. 58, 1968, pp. 1634-1637.
  • [75]S. Twomey: “Comparison of constrained linear inversion and an iterative non-linear algorithm applied to the indirect estimation of particle size distributions”. J. Comput. Phys., vol. 18, 1975, pp. 188-200.
  • [76]W. Winklmayr, H. Wang, W. John: “Adaptation of the Twomey Algorithm to the Inversion of Cascade Impactor Data”. J. Aerosol Sci. and Technol., vol. 13, 1990, pp. 322-331.
  • [77]G.R. Markowski: “Improving Twomey’s Algorithm for Inversion of Aerosol Measurement Data”. Aerosol Sci. and Technol., vol. 7, 1987, pp. 127-141.
  • [78]A. Ben-David, B.M Herman: “Method for determining particle size distribution by non-linear inversion of backscattered radiation”. Appl. Opt., vol. 24, 1985, pp. 1037-1042.
  • [79]G. Ramachandran, M. Kandlikar: “Bayesian analysis for inversion of aerosol size distribution data”. J. Aerosol Sci., vol. 27, 1996, pp. 1099-1112.
  • [80]I.P. Zakharov, S.V. Vodotyka: “Application of Monte Carlo Simulation for the Evaluation of Measurements Uncertaint. Metrol Meas Syst, vol. 15, no. 1, 2008, pp. 117-124.
  • [81]M. Boćkowska, A. Żuchowski: “An Optimal Degree of Complexity of a Simplified Model”. Metrol Meas Syst, vol. 14, no. 2, 2007, pp. 283-290.
  • [82]T. Dang: “An Iterative Parameter Estimation Method for Observation Models with Nonlinear Constraints”. Metrol Meas Syst, vol. 15, no. 4, 2008, pp. 421-432.
  • [83]G.H. Golub, U. von Matt: “Quadratically constrained least squares and quadratic problems”. Numer. Math., vol. 59, 1991, pp. 561-580.
  • [84]B. Martos: Nonlinear Programming. Theory and Methods. Akadémiai Kiadó, Budapest, 1975.
  • [85]W.I. Zangwill: Nonlinear Programming: A Unified Approach. Prentice-Hall, Englewood Cliffs, New Jersey, 1969.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0059-0001
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.